{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c3efd8d",
   "metadata": {},
   "source": [
    "## 时间序列\n",
    "时间序列是把同一事件的历史统计资料按照时间顺序排列起来得到的一组数据序列，时间序列预测法就是通过编制和分析时间序列，根据时间序列所反映出来的发展过程、方向和趋势，进行类推或延伸，借以预测下一段时间或以后若干年内可能达到的水平。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c9faa82a",
   "metadata": {},
   "source": [
    "### （1） 自回归（Autoregressive model/AR）模型\n",
    "\n",
    "自回归模型(Autoregressive Model，简称 **AR 模型**)仅通过时间序列变量的自身历史观测值来反映有关因素对预测目标的影响和作用，不受模型变量相互独立的假设条件约束，所构成的模型可以消除普通回归预测方法中由于自变量选择、多重共线性等造成的困难，是最常见的平稳时间序列模型之一。\n",
    "\n",
    "![picture_模型AR](picture_模型AR.png \"picture_模型AR\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e21f0446",
   "metadata": {},
   "source": [
    "### （2） 滑动平均（moving average model/MA）模型\n",
    "滑动平均（moving average model/MA）模型也称移动平均模型，是用过去各个时期的随机干扰或预测误差的线性组合来表达当前预测值。相比之下，AR模型是通过分析研究历史数据对当前数据的影响进行建模。\n",
    "\n",
    "![picture_模型MA](picture_模型MA.png \"picture_模型MA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52939c3b",
   "metadata": {},
   "source": [
    "### （3） 自回归滑动平均（Autoregressive moving average model/ARMA）模型\n",
    "ARMA模型就是AR模型和MA模型混合。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736a7d6b",
   "metadata": {},
   "source": [
    "### （4） 自回归差分滑动平均（Autoregressive Integrated Moving Average model/ARIMA）模型\n",
    "ARIMA模型也是时间序列预测分析常用方法之一，其在ARMA模型基础上考虑了时间序列的差分，ARIMA模型有三个参数ARIMA(p,d,q)，p为自回归AR项数，q为滑动平均MA项数，d为使序列平稳所做的差分次数（阶数）。“差分”一词虽未出现在ARIMA的英文名称中，却是关键步骤。\n",
    "差分后是对序列的差分的结果建立模型而不是真正的序列，例如ARIMA(p,1,q)相当于对差分序列 {x<sub>t</sub> – x<sub>t-1</sub> } 进行 ARMA(p,q) 回归。<sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "023bf328",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aefef4bc",
   "metadata": {},
   "source": [
    "#### (AR模型) 模拟练习6、Excel表格“pydata06”收集股票相关数据，基于“pydata06.xlsx”数据集回答以下问题。\n",
    "106、(单选题)\n",
    "对pydata06的'zn'进行自回归模型拟合，下列AR模型拟合效果图是（ ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "396749e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Date      zn      sb    tax  marza  classify\n",
      "0 2017-12-24  133495  190382  28407   2848         1\n",
      "1 2017-12-27  215404  295343  42077  37862         2\n",
      "2 2017-12-28  212066  291402  39722  39614         3\n",
      "3 2017-12-29  228009  315223   4096  46254         1\n",
      "4 2017-12-30  346387  482336  62944  73005         3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b417f2370>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# AR模型\n",
    "df = pd.read_excel(\"pydata06_1678066150098.xlsx\")\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df = df.sort_values('Date', ascending=True)\n",
    "print(df.head())\n",
    "\n",
    "data = df['zn'].to_numpy()\n",
    "model = ARIMA(data, order=(20, 0, 0), enforce_stationarity=True)  # 20阶AR\n",
    "result_model = model.fit()\n",
    "\n",
    "fig, ax = plt.subplots() \n",
    "x = df['Date'].to_numpy()\n",
    "y1 = df['zn'].to_numpy()\n",
    "y2 = result_model.fittedvalues\n",
    "ax.plot(x, y1, color='blue', linestyle='-', label='True')\n",
    "ax.plot(x, y2, color='red', linestyle='--', label='model')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae04e375",
   "metadata": {},
   "source": [
    "#### (MA模型) 模拟练习6、Excel表格“pydata02”收集了某城市的天气数据，基于“pydata02.xlsx”数据集回答以下问题。 \n",
    "106、(单选题)\n",
    "对pydata02的大气温度进行滑动平均模型拟合，下列为1阶模型拟合效果图是（A）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16db0685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     日期  大气温度  气象站气压  相对湿度  类别\n",
      "223 2022-02-01 02:00:00   5.3  718.9    77   2\n",
      "222 2022-02-01 05:00:00   4.9  717.8    83   1\n",
      "221 2022-02-01 08:00:00   4.6  718.2    91   1\n",
      "220 2022-02-01 11:00:00   5.7  719.5    82   3\n",
      "219 2022-02-01 14:00:00   6.4  717.7    79   2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b41953a60>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 导入数据\n",
    "df = pd.read_excel(\"pydata02_1678066030739.xlsx\")\n",
    "df['日期'] = pd.to_datetime(df['日期'])\n",
    "df = df.sort_values('日期', ascending=True)\n",
    "print(df.head())\n",
    "\n",
    "# MA模型\n",
    "data = df['大气温度'].to_numpy()\n",
    "model = ARIMA(data, order=(0, 0, 1))  # 1阶MA\n",
    "result_model = model.fit()\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "x = df['日期'].to_numpy()\n",
    "y1 = df['大气温度'].to_numpy()\n",
    "y2 = result_model.fittedvalues\n",
    "ax.plot(x, y1, color='blue', linestyle='-', label='True')\n",
    "ax.plot(x, y2, color='red', linestyle='--', label='model')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e47c941",
   "metadata": {},
   "source": [
    "#### (差分模型) 模拟练习22、Excel表格“pydata08”收集股票相关数据，基于“pydata08.xlsx”数据集回答以下问题。\n",
    "106、(单选题)\n",
    "对pydata08的'AAPL'进行差分，下列为二阶差分的结果图是（A）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "742b9e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Date       AAPL    MSFT   INTC  classify\n",
      "0 2010-01-04  30.572827  30.950  20.88         1\n",
      "1 2010-01-05  30.625684  30.960  20.87         2\n",
      "2 2010-01-06  30.138541  30.770  20.80         3\n",
      "3 2010-01-07  30.082827  30.452  20.60         1\n",
      "4 2010-01-08  30.282827  30.660  20.83         3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b3e541cd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ARIMA模型\n",
    "df = pd.read_excel(\"pydata08_1678066195349.xlsx\")\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df = df.sort_values('Date', ascending=True)\n",
    "print(df.head())\n",
    "\n",
    "df['diff1'] = df['AAPL'].diff(1)  # 一阶差分\n",
    "df['diff2'] = df['diff1'].diff(1)  # 二阶差分\n",
    "data = df['diff2'].to_numpy()\n",
    "model = ARIMA(data, order=(0,2,0))  # 2阶差分模型\n",
    "result_model = model.fit()\n",
    "\n",
    "fig, ax = plt.subplots() \n",
    "x = df['Date'].to_numpy()\n",
    "y = result_model.fittedvalues\n",
    "ax.plot(x, y, color='blue', linestyle='-', label='model')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac23ce",
   "metadata": {},
   "source": [
    "#### (ARMA模型) 模拟练习2、Excel表格“pydata02”收集了某城市的天气数据，基于“pydata02.xlsx”数据集回答以下问题。 \n",
    "\n",
    "106、(单选题)\n",
    "对pydata02的大气温度进行ARMA模型拟合，下列为3阶AR和2阶MA模型的拟合效果图是（ ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f3aa2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     日期  大气温度  气象站气压  相对湿度  类别\n",
      "223 2022-02-01 02:00:00   5.3  718.9    77   2\n",
      "222 2022-02-01 05:00:00   4.9  717.8    83   1\n",
      "221 2022-02-01 08:00:00   4.6  718.2    91   1\n",
      "220 2022-02-01 11:00:00   5.7  719.5    82   3\n",
      "219 2022-02-01 14:00:00   6.4  717.7    79   2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\python\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b446f0160>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 导入数据\n",
    "df = pd.read_excel(\"pydata02_1678066030739.xlsx\")\n",
    "df['日期'] = pd.to_datetime(df['日期'])\n",
    "df = df.sort_values('日期', ascending=True)\n",
    "print(df.head())\n",
    "\n",
    "# ARMA模型\n",
    "data = df['大气温度'].to_numpy()\n",
    "model = ARIMA(data, order=(3, 0, 2))  # 3阶AR和2阶MA\n",
    "result_model = model.fit()\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "x = df['日期'].to_numpy()\n",
    "y1 = df['大气温度'].to_numpy()\n",
    "y2 = result_model.fittedvalues\n",
    "ax.plot(x, y1, color='blue', linestyle='-', label='True')\n",
    "ax.plot(x, y2, color='red', linestyle='--', label='model')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5164c481",
   "metadata": {},
   "source": [
    "#### (ARIMA模型) 模拟练习9、Excel表格“pydata09”收集股票相关数据，基于“pydata09.xlsx”数据集回答以下问题。\n",
    "106、(单选题)\n",
    "对pydata09的'AMZN'进行ARIMA模型拟合，下列为3阶AR、2阶MA、1阶差分模型的拟合效果图是（ D）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fda1b0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b4476cca0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ARIMA模型\n",
    "df = pd.read_excel(\"pydata09_1678066221127.xlsx\")\n",
    "\n",
    "df['diff1'] = df['AMZN'].diff(1)  # 一阶差分\n",
    "data = df['diff1'].to_numpy()\n",
    "model = ARIMA(data, order=(3, 1, 2))  # 3阶AR、2阶MA、1阶差分模型\n",
    "result_model = model.fit()\n",
    "\n",
    "fig, ax = plt.subplots() \n",
    "x = df['Date'].to_numpy()\n",
    "y1 = df['AMZN'].to_numpy()\n",
    "y2 = result_model.fittedvalues\n",
    "ax.plot(x, y1, color='blue', linestyle='-', label='True')\n",
    "ax.plot(x, y2, color='red', linestyle='--', label='modle_AR')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "922d2b1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
